The tools to perform a large deviation analysis of time averages of
climatic observables are thus available; however, a systematic description
and evaluation of such tools specifically framed for climate studies
is still lacking. In this paper we describe how to use large deviation principles and
rare event algorithms in a climatic study. The paper is structured
as follows. First we introduce the basic formalism of Donsker-Varadhan
large deviation theory. Then we provide a detailed description of
how to compute large deviation functions from time series of an observable
of a dynamical system, following [1]}. We give a
practical example of such analysis, computing large deviation functions
of the time average of the European surface temperature from long
simulations with the climate model Plasim [2]}.
We then show how the tails of the large deviation functions can be
computed very efficiently using the rare event algorithm we have used
in [3]}, giving here more details about the method and
focusing on the role of large deviation theory. Finally we discuss
the potential for further studies.
